无线传感器网络中基于V-BLAST的虚拟MIMO通信研究
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摘要
无线传感器网络是一门新兴的技术，其综合了传感器技术、嵌入式计算技术、分布式信息处理技术和无线通信技术，来实现对分布区域内各种环境或监测对象的信息进行协作地实时监测、感知和采集。无线传感器网络一般都部署在环境复杂的地区，传感器携带的电池难以被替换，因此一旦传感器节点的电源耗尽会直接影响到整个网络功能的实现。所以针对此种能量受限型网络，能量的有效性成了首要问题。保证传感器网络中各个节点能量的高效使用，延长整个网络的寿命，是传感器网络需要考虑的重要方面。把新一代移动通信系统的关键技术多入多出(MIMO)技术应用到传感器网络中，形成虚拟多入多出(VMIMO)的通信系统，比传统的单入单出(SISO)的通信模型具有更好的能量有效性。空时编码中的Alamouti码因其编码和译码非常简单，所以在能量受限的传感器网络得到广泛的应用。但由于Alamouti码需要在传感器的发射端进行传感器节点的联合编码合作通信过程，需要消耗一定的能量，因此更简单的基于V-BLAST虚拟MIMO传感器网络通信系统模型在能量有效性方面的优势就显现出来。本文就是在V-BLAST模型的基础上，在接收端引入了MMSE-OSIC算法，来节省整个传感器网络的能量消耗。论文的主要研究工作如下：
1、回顾了无线传感器网络发展的历史，同时对整个无线传感器网络进行了介绍和概括，研究和分析了无线传感器网络的目前的研究热点与主要问题。
2、在研究多入多出(MIMO)原理的基础上，对空时编码技术尤其是分层空时码及其检测算法做了深入研究。
3、在研究了多入多出(MIMO)技术应用到能量受限分布广泛的无线传感器网络中，形成虚拟多入多出(VMIMO)的通信系统模型的基础上，本文采用基于V-BLAST虚拟MIMO传感器网络通信系统为模型，主要研究接收端V-BLAST处理检测过程，并在接收端引入MMSE-OSIC算法，使网络发射端的每个节点都能以更小的功率来得到QR算法，MMSE算法可以达到的输出要求，来节省发射端每个节点的发射数据的能量消耗，提高整个网络的能量利用效率。
4、与传统的无线网络中的能量消耗主要集中在实际无线发射的能量消耗所不同，在能量受限的传感器网络中，电路模块的能量消耗不可忽略。本文通过对无线传感器网络中基于V-BLAST虚拟MIMO通信模型研究，在接收端引入MMSE-OSIC算法后节省发射端能量消耗的基础上，结合发射端每个节点发射数据的电路能量消耗，对整个通信过程中的能量消耗进行研究分析。通过实验仿真分析，MMSE-OSIC算法的引入有效的节省了传感器网络在整个通信过程的能量消耗，对延长网络寿命起了积极作用。
Wireless Sensor Network (WSN) is an emerging technology, which incorporatessensor techniques, nested computation techniques, distributed computation techniquesand wireless communication techniques. Wireless Sensor Network can be used for testing,sensing, collecting and processing information of monitored objects. As Wireless SensorNetwork is generally deployed in very complex environment and meanwhile sensorbatteries are difficult to replace, so the whole network function will be influenced ifsensor node batteries run out. As to this energy-confined network, energy efficiency is aprincipal issue. It is important to ensure energy efficient usage of every node in thenetwork so as to prolong the whole network lifetime, which is a key aspect to WirelessSensor Network.
By combing the key mobile technology of new generation communication MultipleInput Multiple Output (MIMO) with Wireless Sensor Network, the VirtualMIMO(VMIMO) communication system is formed, which shows better energy efficiencythan traditional Single Input Single Output (SISO) communication model. Due to simpleencoding and decoding process, MIMO space-time code Alamouti code is widely appliedin energy-confined sensor networks. As Alamouti scheme requires transmitter-side sensorjoint coding cooperation, which will leads to energy-consuming. So simpler VirtualMIMO sensor network communication system model based on Vertical Bell LaboratoriesLayered Space Time (V-BLAST) processing shows its energy efficient advantage. Andwe introduce Minimum Mean Square Error-Ordered Successive Interference Cancellation(MMSE-OSIC) algorithm into model above in, order to save the energy consumption ofthe whole sensor network. The main content in the research of this thesis includes:
1、Review the history of Wireless Sensor Network, introduce and make a summaryon the whole Wireless Sensor Network, then study and analyze the recent main problemand hotspot of Wireless Sensor Network.
2、On the basic analysis of MIMO principle, we make the further exploration on thespace-time code technique, especially in layer space-time code and its detectionalgorithm.
3、On the basis of applying the MIMO technology into the energy-confined, widelydispread Wireless Sensor Network to form the Virtual MIMO communication systemmodel, we adopt Virtual MIMO sensor network communication system model based onV-BLAST detection processing. Then we study V-BLAST detection processing onreceiver side and introduce the MMSE-OSIC algorithm on the receiver side in order tomake every node on the transmitter side consumes less power to meet the outputrequirement comparing with MMSE and QR algorithm, which will enhance energy usingefficiency of the whole network.
4、In the traditional wireless network the energy consumption mainly concentrates inenergy consumption caused by practical wireless transmission. But in energy-confinedWireless Sensor Network the energy consumption of circuit module cannot be neglected,which is different from traditional wireless network. Through the research on VMIMOcommunication model based on V-BLAST processing in the Wireless Sensor Network,the transmitter side energy consuming is saved by applying with MMSE-OSIC algorithmon receiver side. On the basis of saved energy on the transmitter side, we make theanalysis about total energy consumption combing with the transmitter side circuit moduleenergy consumption during the whole communication process. Based on the analysis ofthe experimental simulation, the introduction of MMSE-OSIC algorithm acquiresefficiency on saving the total energy consumption during the whole communicationprocess in the Wireless Sensor Network and makes the positive effect on prolongingnetwork life.
1、回顾了无线传感器网络发展的历史，同时对整个无线传感器网络进行了介绍和概括，研究和分析了无线传感器网络的目前的研究热点与主要问题。
2、在研究多入多出(MIMO)原理的基础上，对空时编码技术尤其是分层空时码及其检测算法做了深入研究。
3、在研究了多入多出(MIMO)技术应用到能量受限分布广泛的无线传感器网络中，形成虚拟多入多出(VMIMO)的通信系统模型的基础上，本文采用基于V-BLAST虚拟MIMO传感器网络通信系统为模型，主要研究接收端V-BLAST处理检测过程，并在接收端引入MMSE-OSIC算法，使网络发射端的每个节点都能以更小的功率来得到QR算法，MMSE算法可以达到的输出要求，来节省发射端每个节点的发射数据的能量消耗，提高整个网络的能量利用效率。
4、与传统的无线网络中的能量消耗主要集中在实际无线发射的能量消耗所不同，在能量受限的传感器网络中，电路模块的能量消耗不可忽略。本文通过对无线传感器网络中基于V-BLAST虚拟MIMO通信模型研究，在接收端引入MMSE-OSIC算法后节省发射端能量消耗的基础上，结合发射端每个节点发射数据的电路能量消耗，对整个通信过程中的能量消耗进行研究分析。通过实验仿真分析，MMSE-OSIC算法的引入有效的节省了传感器网络在整个通信过程的能量消耗，对延长网络寿命起了积极作用。
Wireless Sensor Network (WSN) is an emerging technology, which incorporatessensor techniques, nested computation techniques, distributed computation techniquesand wireless communication techniques. Wireless Sensor Network can be used for testing,sensing, collecting and processing information of monitored objects. As Wireless SensorNetwork is generally deployed in very complex environment and meanwhile sensorbatteries are difficult to replace, so the whole network function will be influenced ifsensor node batteries run out. As to this energy-confined network, energy efficiency is aprincipal issue. It is important to ensure energy efficient usage of every node in thenetwork so as to prolong the whole network lifetime, which is a key aspect to WirelessSensor Network.
By combing the key mobile technology of new generation communication MultipleInput Multiple Output (MIMO) with Wireless Sensor Network, the VirtualMIMO(VMIMO) communication system is formed, which shows better energy efficiencythan traditional Single Input Single Output (SISO) communication model. Due to simpleencoding and decoding process, MIMO space-time code Alamouti code is widely appliedin energy-confined sensor networks. As Alamouti scheme requires transmitter-side sensorjoint coding cooperation, which will leads to energy-consuming. So simpler VirtualMIMO sensor network communication system model based on Vertical Bell LaboratoriesLayered Space Time (V-BLAST) processing shows its energy efficient advantage. Andwe introduce Minimum Mean Square Error-Ordered Successive Interference Cancellation(MMSE-OSIC) algorithm into model above in, order to save the energy consumption ofthe whole sensor network. The main content in the research of this thesis includes:
1、Review the history of Wireless Sensor Network, introduce and make a summaryon the whole Wireless Sensor Network, then study and analyze the recent main problemand hotspot of Wireless Sensor Network.
2、On the basic analysis of MIMO principle, we make the further exploration on thespace-time code technique, especially in layer space-time code and its detectionalgorithm.
3、On the basis of applying the MIMO technology into the energy-confined, widelydispread Wireless Sensor Network to form the Virtual MIMO communication systemmodel, we adopt Virtual MIMO sensor network communication system model based onV-BLAST detection processing. Then we study V-BLAST detection processing onreceiver side and introduce the MMSE-OSIC algorithm on the receiver side in order tomake every node on the transmitter side consumes less power to meet the outputrequirement comparing with MMSE and QR algorithm, which will enhance energy usingefficiency of the whole network.
4、In the traditional wireless network the energy consumption mainly concentrates inenergy consumption caused by practical wireless transmission. But in energy-confinedWireless Sensor Network the energy consumption of circuit module cannot be neglected,which is different from traditional wireless network. Through the research on VMIMOcommunication model based on V-BLAST processing in the Wireless Sensor Network,the transmitter side energy consuming is saved by applying with MMSE-OSIC algorithmon receiver side. On the basis of saved energy on the transmitter side, we make theanalysis about total energy consumption combing with the transmitter side circuit moduleenergy consumption during the whole communication process. Based on the analysis ofthe experimental simulation, the introduction of MMSE-OSIC algorithm acquiresefficiency on saving the total energy consumption during the whole communicationprocess in the Wireless Sensor Network and makes the positive effect on prolongingnetwork life.
引文
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[2] J. R. Polastre: Design and implementation of wireless sensor networks for habitat monitoring. Master's thesis, University of California at Berkeley, 2003.
[3] IF Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci: A survey on sensor networks Communications Magazine, IEEE, 2002, 40(8): 102-114
[4] 马祖长，孙怡宁，梅涛：无线传感器网络综述，通信学报，2004，Vol．25 No．4：114-124
[5] 刘强，卢东昕，张焕水，那成亮：无线传感器网络拓扑控制分析，电信快报，2006．06 pp：36-38
[6] Heinzelman W, Chandrakasan A, Balakrishnan H. An Applications Specific Protocol Architecture for Wireless Microsensor Networks [J]. IEEE Trans on Wireless Communications, 2002, 1(4):660-667.
[7] Yu Y, Govindan R, Estrin D. Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. UCLA Computer Science Department Technical Report UCLA/CSD-TR-01-0023, May 2001.
[8] Newsome J, Song D. GEM: Graph Embeding for routing and data-centric storage in sensor networks without geographic information. In: Proc 1st ACM Conf on Embedded Networked Sensor System (SenSys'03), Redwood, CA. November 2003.
[9] Kumar R, Wolenetz M, Agarwalla B, Shin J, Hutto P, Paul A, Ramachandran U. DFuse: A framework for distributed data fusion. In: Proc 1st ACM Conf on Embedded Networked Sensor System (SenSys'03),Redwood,CA. November 2003. 114-125.
[10] B Deb, S Bhatnagar, B Nath. RelnForM: reliable information forwarding using multiple paths in sensor networks. Local Computer Networks, 2003. LCN'03. Proceedings 28th Annual IEEE International Conference on, 2003, pp: 406-415.
[11] 孙超，张世庆，张西良：无线传感器网络MAC协议研究，传感器世界[J]．2006， Vol. 12, No. 8,pp:31-34.
[12] Wood A D, Stankovic J A. Denial of service in sensor networks. IEEE Computer, 2002, 35(10):54-62.
[13]Perrig A, Szewczyk R, et al. SPINS: Security protocols for sensor networks. Wireless Networks, 2002, 8(5):521-534.
[14]Chan H W, Perrig A, Song D. Random key predistribution schemes for sensor networks. In: Proc 2003 IEEE Symp on Security and Privacy, 2003, 197.
[15]Elson J, Griod L, Esrein D. Fine-grained network time synchronization using reference broadcasts. In: Proc 5th Symp Operating System Design and Implementation (OSDI 2002), Boston, MA. Decemeber 2002.
[16]Ganeriwal S, Kumar R, Srivastava M B. Timing-sync protocol for sensor networks. In: Proc 1st Int'l Conf on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, CA. November 5-7, 2003. 138-139.
[17] Ganeriwal S, Kumar R, Adlakha S, Srivastava M B. Network-wide time synchronization in sensor networks. Networked and Embedded System Lab(NESL), UCLA Technical Report 01-01-2003.
[18]Priyantha N B, Balakrishnam H, Demaine E, Teller S. Anchor-free distribution localization in sensor networks. Technical Report MIT-LCS-TR-892, MIT Lab for Computer Science, April 2003.
[19]Krishnamachari B, Estrin D, Wicher S. Impact of data aggregation in wireless sensor networks. In: Proc 22nd Int'l Conf on Distributed Computing Systems, Vienna, Austria, July 2002.
[20]Madden S. The Design and Evaluation of a Query Processing Architecture for Sensor Networks. Ph.D Thesis, UC Berkeley. Oct. 2002.
[21]Bonnet P, Gehrke J E, Seshadri P. Towards Sensor database system. In: Proc 2nd Int'l Conf on Mobile Data Management, Hong Kong, January 2001. LNCS 1987, London: SpringerVerlag,2001.
[22]Sinha A, Chandrakasan A. Dynamic power management in wireless sensor network. IEEE Design and Test of Computer,2001, 18(2):62-74.
[23] W. Ye, J. Heidemann, and D. Estrin. An energy-efficient MAC protocol for wireless sensor networks, in IEEE INFOCOM, 2002, pp. 1567-1576.
[24] Raminder P. Mann, Kamesh R. Namuduri, Ravi Pendse. Energy-Aware Routing Protocol for Ad Hoc Wireless Sensor Networks. EURASIP Journal on Wireless Communications and Networking. 2005 (05), pp: 635-644.
[25] Telatar I E. Capacity of multi-antenna Gaussian channel [J]. European Transactions on Telecommunications, 1999, 10 (6):585-595.
[26] Rapajic P B. and Popesu D. Information capacity of a random signature multiple-input multiple-output channel.[J] IEEE Transaction on Information Theory, vol. 48, August 2000, pp: 1245-1248.
[27] Byers G J, Takawira F. Spatially and Temporally Correlated MIMO Channels: Modeling and Capacity Analysis.[J] IEEE Transactions on Vehicular Technology,Vol.53,No.3 ,MAY 2004,634-643.
[28] Foschini G J. Layered space-time architecture for wireless communication in a fading environment using multi-element antennas[J].Bell Labs Technical Journal, 1996,1 (2)pp:41-59.
[29] 樊昌信，詹道庸，徐炳祥等．通信原理：第四版[M]．北京：国防工业出版社1995：48-51．
[30] Golden G D,Foschini G J,Valenzuela R A ,et al. Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture, Electronics Leters, 1999;35(1).
[31] V.Tarokh, N.Seshadri. and A.R.Calderbank. Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Trans. Inform. Theory Vol.44, No.2, pp.744-765, Mar. 1998.
[32] P W Wolniansky, G J Foschini, G D Golden,et al .V -BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel[J]. International Symposiumon Signals, Systems and Electronics, 1998: 295-300.
[33] S. Verdu: Spectral efficiency in the wide band regime, IEEE Trans. Information Theory, vol.48, pp. 1319-1343, June 2002.
[34] A. E. Gamal, C. Nair, B. Prabhakar, E. Uysal-Biyikoglu and S. Zahedi: Energy-efficient scheduling of packet transmissions over wireless networks, IEEE Infocom Conference on Computer Communications, NY City, June 2002,vol. 3: 1773-1782.
[35] Shuguang Cui, Andrea J.Goldsmith, Abroad Bahai, Energy-constrained modulation optimization, IEEE Transactions on Wireless Communications, Sep. 2005 pp: 1-11。
[36] Siavash M. Alamouti: A Simple Transmit Diversity Technique for Wireless Communications. IEEE Journal on Select Areas in Communications, vol. 16, No. 8, October 1998: 1451-1458
[37] A.Paulraj, R.Nabar, and D.Gore, Introduction to Space-Time Wireless Communication, Cambridge, U.K.: Cambridge Univ. Press 2003.
[38] S. Cui, A. J. Goldsmith, and A. Bahai: Energy-efficiency of MIMO and Cooperative MIMO Techniques in Sensor Networks, IEEE Journal. Select. Areas. Commun. Vol.22 No.6 August 2004: 1089-1098.
[39] Sudharman K. Jayaweera: An energy-efficient virtual MIMO architecture based on V-BLAST processing for distributed wireless sensor networks. 1st IEEE International Conf on Sensor and Ad-hoc Communications and Networks (SECON), Santa Clara, CA, Oct 2004: 299-308.
[40] S. Cui, A. J. Goldsmith, and A. Bahai, Energy-constrained modulation optimization, IEEE Transactions On Wireless Communications, December 2003: 1-11.
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[44] 曹志刚，钱亚生，现代通信原理，清华大学出版社．
[45] S.Cui, A.J.Goldsmith, and A. Bahai. Energy constrained modulation optimization for both uncoded and coded systems. IEEE Trans. Commun. 2003 submitted.
[46] E. Lauwers and G. Gielen, Power estimation methods for analog circuits for architectural exploration of integrated system. IEEE Trans. VLSI System,vol. 10, pp. 155-162, April 2002.
[2] J. R. Polastre: Design and implementation of wireless sensor networks for habitat monitoring. Master's thesis, University of California at Berkeley, 2003.
[3] IF Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci: A survey on sensor networks Communications Magazine, IEEE, 2002, 40(8): 102-114
[4] 马祖长，孙怡宁，梅涛：无线传感器网络综述，通信学报，2004，Vol．25 No．4：114-124
[5] 刘强，卢东昕，张焕水，那成亮：无线传感器网络拓扑控制分析，电信快报，2006．06 pp：36-38
[6] Heinzelman W, Chandrakasan A, Balakrishnan H. An Applications Specific Protocol Architecture for Wireless Microsensor Networks [J]. IEEE Trans on Wireless Communications, 2002, 1(4):660-667.
[7] Yu Y, Govindan R, Estrin D. Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. UCLA Computer Science Department Technical Report UCLA/CSD-TR-01-0023, May 2001.
[8] Newsome J, Song D. GEM: Graph Embeding for routing and data-centric storage in sensor networks without geographic information. In: Proc 1st ACM Conf on Embedded Networked Sensor System (SenSys'03), Redwood, CA. November 2003.
[9] Kumar R, Wolenetz M, Agarwalla B, Shin J, Hutto P, Paul A, Ramachandran U. DFuse: A framework for distributed data fusion. In: Proc 1st ACM Conf on Embedded Networked Sensor System (SenSys'03),Redwood,CA. November 2003. 114-125.
[10] B Deb, S Bhatnagar, B Nath. RelnForM: reliable information forwarding using multiple paths in sensor networks. Local Computer Networks, 2003. LCN'03. Proceedings 28th Annual IEEE International Conference on, 2003, pp: 406-415.
[11] 孙超，张世庆，张西良：无线传感器网络MAC协议研究，传感器世界[J]．2006， Vol. 12, No. 8,pp:31-34.
[12] Wood A D, Stankovic J A. Denial of service in sensor networks. IEEE Computer, 2002, 35(10):54-62.
[13]Perrig A, Szewczyk R, et al. SPINS: Security protocols for sensor networks. Wireless Networks, 2002, 8(5):521-534.
[14]Chan H W, Perrig A, Song D. Random key predistribution schemes for sensor networks. In: Proc 2003 IEEE Symp on Security and Privacy, 2003, 197.
[15]Elson J, Griod L, Esrein D. Fine-grained network time synchronization using reference broadcasts. In: Proc 5th Symp Operating System Design and Implementation (OSDI 2002), Boston, MA. Decemeber 2002.
[16]Ganeriwal S, Kumar R, Srivastava M B. Timing-sync protocol for sensor networks. In: Proc 1st Int'l Conf on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, CA. November 5-7, 2003. 138-139.
[17] Ganeriwal S, Kumar R, Adlakha S, Srivastava M B. Network-wide time synchronization in sensor networks. Networked and Embedded System Lab(NESL), UCLA Technical Report 01-01-2003.
[18]Priyantha N B, Balakrishnam H, Demaine E, Teller S. Anchor-free distribution localization in sensor networks. Technical Report MIT-LCS-TR-892, MIT Lab for Computer Science, April 2003.
[19]Krishnamachari B, Estrin D, Wicher S. Impact of data aggregation in wireless sensor networks. In: Proc 22nd Int'l Conf on Distributed Computing Systems, Vienna, Austria, July 2002.
[20]Madden S. The Design and Evaluation of a Query Processing Architecture for Sensor Networks. Ph.D Thesis, UC Berkeley. Oct. 2002.
[21]Bonnet P, Gehrke J E, Seshadri P. Towards Sensor database system. In: Proc 2nd Int'l Conf on Mobile Data Management, Hong Kong, January 2001. LNCS 1987, London: SpringerVerlag,2001.
[22]Sinha A, Chandrakasan A. Dynamic power management in wireless sensor network. IEEE Design and Test of Computer,2001, 18(2):62-74.
[23] W. Ye, J. Heidemann, and D. Estrin. An energy-efficient MAC protocol for wireless sensor networks, in IEEE INFOCOM, 2002, pp. 1567-1576.
[24] Raminder P. Mann, Kamesh R. Namuduri, Ravi Pendse. Energy-Aware Routing Protocol for Ad Hoc Wireless Sensor Networks. EURASIP Journal on Wireless Communications and Networking. 2005 (05), pp: 635-644.
[25] Telatar I E. Capacity of multi-antenna Gaussian channel [J]. European Transactions on Telecommunications, 1999, 10 (6):585-595.
[26] Rapajic P B. and Popesu D. Information capacity of a random signature multiple-input multiple-output channel.[J] IEEE Transaction on Information Theory, vol. 48, August 2000, pp: 1245-1248.
[27] Byers G J, Takawira F. Spatially and Temporally Correlated MIMO Channels: Modeling and Capacity Analysis.[J] IEEE Transactions on Vehicular Technology,Vol.53,No.3 ,MAY 2004,634-643.
[28] Foschini G J. Layered space-time architecture for wireless communication in a fading environment using multi-element antennas[J].Bell Labs Technical Journal, 1996,1 (2)pp:41-59.
[29] 樊昌信，詹道庸，徐炳祥等．通信原理：第四版[M]．北京：国防工业出版社1995：48-51．
[30] Golden G D,Foschini G J,Valenzuela R A ,et al. Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture, Electronics Leters, 1999;35(1).
[31] V.Tarokh, N.Seshadri. and A.R.Calderbank. Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Trans. Inform. Theory Vol.44, No.2, pp.744-765, Mar. 1998.
[32] P W Wolniansky, G J Foschini, G D Golden,et al .V -BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel[J]. International Symposiumon Signals, Systems and Electronics, 1998: 295-300.
[33] S. Verdu: Spectral efficiency in the wide band regime, IEEE Trans. Information Theory, vol.48, pp. 1319-1343, June 2002.
[34] A. E. Gamal, C. Nair, B. Prabhakar, E. Uysal-Biyikoglu and S. Zahedi: Energy-efficient scheduling of packet transmissions over wireless networks, IEEE Infocom Conference on Computer Communications, NY City, June 2002,vol. 3: 1773-1782.
[35] Shuguang Cui, Andrea J.Goldsmith, Abroad Bahai, Energy-constrained modulation optimization, IEEE Transactions on Wireless Communications, Sep. 2005 pp: 1-11。
[36] Siavash M. Alamouti: A Simple Transmit Diversity Technique for Wireless Communications. IEEE Journal on Select Areas in Communications, vol. 16, No. 8, October 1998: 1451-1458
[37] A.Paulraj, R.Nabar, and D.Gore, Introduction to Space-Time Wireless Communication, Cambridge, U.K.: Cambridge Univ. Press 2003.
[38] S. Cui, A. J. Goldsmith, and A. Bahai: Energy-efficiency of MIMO and Cooperative MIMO Techniques in Sensor Networks, IEEE Journal. Select. Areas. Commun. Vol.22 No.6 August 2004: 1089-1098.
[39] Sudharman K. Jayaweera: An energy-efficient virtual MIMO architecture based on V-BLAST processing for distributed wireless sensor networks. 1st IEEE International Conf on Sensor and Ad-hoc Communications and Networks (SECON), Santa Clara, CA, Oct 2004: 299-308.
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